Even with a fix for the bug mentioned above, if the nearest neighbor index file is larger than 2GB in size, Annoy may not be able to read the data back in. This should only occur with very large or high-dimensional datasets. The nearest neighbor search will fail under these conditions. A work-around is to set n_threads = 0, because the index will not be written to disk and re-loaded under these circumstances, at the cost of a longer search time. Alternatively, set the pca parameter to reduce the dimensionality or lower n_trees, both of which will reduce the size of the index on disk. However, either may lower the accuracy of the nearest neighbor results.
Initial CRAN release.
tmpdir, which allows the user to specify the temporary directory where nearest neighbor indexes will be written during Annoy nearest neighbor search. The default is base::tempdir(). Only used if n_threads > 1 and nn_method = "annoy".Fixed an issue with lvish where there was an off-by-one error when calculating input probabilities.
Added a safe-guard to lvish to prevent the gaussian precision, beta, becoming overly large when the binary search fails during perplexity calibration.
The lvish perplexity calibration uses the log-sum-exp trick to avoid numeric underflow if beta becomes large.
pcg_rand. If TRUE (the default), then a random number generator from the PCG family is used during the stochastic optimization phase. The old PRNG, a direct translation of an implementation of the Tausworthe “taus88” PRNG used in the Python version of UMAP, can be obtained by setting pcg_rand = FALSE. The new PRNG is slower, but is likely superior in its statistical randomness. This change in behavior will be break backwards compatibility: you will now get slightly different results even with the same seed.fast_sgd. If TRUE, then the following combination of parameters are set: n_sgd_threads = "auto", pcg_rand = FALSE and approx_pow = TRUE. These will result in a substantially faster optimization phase, at the cost of being slightly less accurate and results not being exactly repeatable. fast_sgd = FALSE by default but if you are only interested in visualization, then fast_sgd gives perfectly good results. For more generic dimensionality reduction and reproducibility, keep fast_sgd = FALSE.init_sdev which specifies how large the standard deviation of each column of the initial coordinates should be. This will scale any input coordinates (including user-provided matrix coordinates). init = "spca" can now be thought of as an alias of init = "pca", init_sdev = 1e-4. This may be too aggressive scaling for some datasets. The typical UMAP spectral initializations tend to result in standard deviations of around 2 to 5, so this might be more appropriate in some cases. If spectral initialization detects multiple components in the affinity graph and falls back to scaled PCA, it uses init_sdev = 1.init_sdev, the init options sspectral, slaplacian and snormlaplacian have been removed (they weren’t around for very long anyway). You can get the same behavior by e.g. init = "spectral", init_sdev = 1e-4. init = "spca" is sticking around because I use it a lot.init = "spca".<random> header. This breaks backwards compatibility even if you set pcg_rand = FALSE.metric = "cosine" results were incorrectly using the unmodified Annoy angular distance.categorical metric (fixes https://github.com/jlmelville/uwot/issues/20).n_components (e.g. approximately 50% faster optimization time with MNIST and n_components = 50).pca_center, which controls whether to center the data before applying PCA. It would be typical to set this to FALSE if you are applying PCA to binary data (although note you can’t use this with setting with metric = "hamming")metric is "manhattan" and "cosine". It’s still not applied when using "hamming" (data still needs to be in binary format, not real-valued).pca and pca_center parameter values for a given data block by using a list for the value of the metric, with the column ids/names as an unnamed item and the overriding values as named items, e.g. instead of manhattan = 1:100, use manhattan = list(1:100, pca_center = FALSE) to turn off PCA centering for just that block. This functionality exists mainly for the case where you have mixed binary and real-valued data and want to apply PCA to both data types. It’s normal to apply centering to real-valued data but not to binary data.umap_transform, where negative sampling was over the size of the test data (should be the training data).verbose = TRUE, log the Annoy recall accuracy, which may help tune values of n_trees and search_k.n_sgd_threads, which controls the number of threads used in the stochastic gradient descent. By default this is now single-threaded and should result in reproducible results when using set.seed. To get back the old, less consistent, but faster settings, set n_sgd_threads = "auto".alpha is now learning_rate.gamma is now repulsion_strength.laplacian and normlaplacian).init options: sspectral, snormlaplacian and slaplacian. These are like spectral, normlaplacian, laplacian respectively, but scaled so that each dimension has a standard deviation of 1e-4. This is like the difference between the pca and spca options.pca: set this to a positive integer to reduce matrix of data frames to that number of columns using PCA. Only works if metric = "euclidean". If you have > 100 columns, this can substantially improve the speed of the nearest neighbor search. t-SNE implementations often set this value to 50.metric: instead of specifying a single metric name (e.g. metric = "euclidean"), you can pass a list, where the name of each item is the metric to use and the value is a vector of the names of the columns to use with that metric, e.g. metric = list("euclidean" = c("A1", "A2"), "cosine" = c("B1", "B2", "B3")) treats columns A1 and A2 as one block, using the Euclidean distance to find nearest neighbors, whereas B1, B2 and B3 are treated as a second block, using the cosine distance.categorical.y may now be a data frame or matrix if multiple target data is available.target_metric, to specify the distance metric to use with numerical y. This has the same capabilities as metric.scale = "Z" To Z-scale each column of input (synonym for scale = TRUE or scale = "scale").scale = "colrange" to scale columns in the range (0, 1).y, you may pass nearest neighbor data directly, in the same format as that supported by X-related nearest neighbor data. This may be useful if you don’t want to use Euclidean distances for the y data, or if you have missing data (and have a way to assign nearest neighbors for those cases, obviously). See the Nearest Neighbor Data Format section for details.ret_nn: when TRUE returns nearest neighbor matrices as a nn list: indices in item idx and distances in item dist. Embedded coordinates are in embedding. Both ret_nn and ret_model can be TRUE, and should not cause any compatibility issues with supervised embeddings.nn_method can now take precomputed nearest neighbor data. Must be a list of two matrices: idx, containing integer indexes, and dist containing distances. By no coincidence, this is the format return by ret_nn.n_components = 1 was broken (https://github.com/jlmelville/uwot/issues/6)init parameter were being modified, in defiance of basic R pass-by-copy semantics.metric = "cosine" is working again for n_threads greater than 0 (https://github.com/jlmelville/uwot/issues/5)August 5 2018. You can now use an existing embedding to add new points via umap_transform. See the example section below.
August 1 2018. Numerical vectors are now supported for supervised dimension reduction.
July 31 2018. (Very) initial support for supervised dimension reduction: categorical data only at the moment. Pass in a factor vector (use NA for unknown labels) as the y parameter and edges with bad (or unknown) labels are down-weighted, hopefully leading to better separation of classes. This works remarkably well for the Fashion MNIST dataset.
July 22 2018. You can now use the cosine and Manhattan distances with the Annoy nearest neighbor search, via metric = "cosine" and metric = "manhattan", respectively. Hamming distance is not supported because RcppAnnoy doesn’t yet support it.